# Automatic Vector-based Road Structure Mapping Using Multi-beam LiDAR

arXiv: https://arxiv.org/abs/1804.07028 · HTML: https://mlsanity.com/paper/1804.07028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [How to Build a Curb Dataset with LiDAR Data for Autonomous Driving](https://mlsanity.com/paper/2110.03968/) (2021)
- [Fusion of neural networks, for LIDAR-based evidential road mapping](https://mlsanity.com/paper/2102.03326/) (2021)

## Abstract (arXiv)

In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We propose to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07. We further applied the mapping system to the urban road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the help of high precision GPS.